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Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data.
| 题名： | Aircraft Anomaly Detection Using Performance Models Trained on Fleet Data. |
| 作者： | Gorinevsky, D.; Martin, R.; Matthews, B. L. |
| 关键词： | A-320 Aircraft; Acceleration Measurement; Ailerons; Aircraft Performance; Algorithms; Anomalies; Bias; Commercial Aircraft; Data Mining; Detection; Failure Analysis; Flight Operations; Mathematical Mo |
| 摘要： | This paper describes an application of data mining technology called Distributed Fleet Monitoring (DFM) to Flight Operational Quality Assurance (FOQA) data collected from a fleet of commercial aircraft. DFM transforms the data into aircraft performance models, flight-to-flight trends, and individual flight anomalies by fitting a multi-level regression model to the data. The model represents aircraft flight performance and takes into account fixed effects: flight-to-flight and vehicle-to-vehicle variability. The regression parameters include aerodynamic coefficients and other aircraft performance parameters that are usually identified by aircraft manufacturers in flight tests. Using DFM, the multi-terabyte FOQA data set with half-million flights was processed in a few hours. The anomalies found include wrong values of competed variables, (e.g., aircraft weight), sensor failures and baises, failures, biases, and trends in flight actuators. These anomalies were missed by the existing airline monitoring of FOQA data exceedances. |
| 报告类型： | 科技报告 |
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